From cc13f3e8e564ebb32b9b1b85f65d744d7cf63ed8 Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Thu, 4 Oct 2018 21:14:42 +1000
Subject: [PATCH] pep examples
---
examples/injection_examples/basic_tutorial.py | 64 +++++++++++--------
.../change_sampled_parameters.py | 6 ++
.../create_your_own_source_model.py | 16 +++--
...reate_your_own_time_domain_source_model.py | 42 ++++++------
.../injection_examples/eccentric_inspiral.py | 45 ++++++-------
.../how_to_specify_the_prior.py | 30 +++++----
.../marginalized_likelihood.py | 31 +++++----
.../sine_gaussian_example.py | 48 +++++++-------
examples/injection_examples/using_gwin.py | 4 +-
.../other_examples/linear_regression_pymc3.py | 4 +-
...near_regression_pymc3_custom_likelihood.py | 8 ++-
.../supernova_example/supernova_example.py | 8 +--
12 files changed, 167 insertions(+), 139 deletions(-)
diff --git a/examples/injection_examples/basic_tutorial.py b/examples/injection_examples/basic_tutorial.py
index 1e90bcddc..e22d38056 100644
--- a/examples/injection_examples/basic_tutorial.py
+++ b/examples/injection_examples/basic_tutorial.py
@@ -1,18 +1,19 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate running parameter estimation on a reduced parameter space for an injected signal.
+Tutorial to demonstrate running parameter estimation on a reduced parameter
+space for an injected signal.
-This example estimates the masses using a uniform prior in both component masses and distance using a uniform in
-comoving volume prior on luminosity distance between luminosity distances of 100Mpc and 5Gpc, the cosmology is WMAP7.
+This example estimates the masses using a uniform prior in both component masses
+and distance using a uniform in comoving volume prior on luminosity distance
+between luminosity distances of 100Mpc and 5Gpc, the cosmology is WMAP7.
"""
from __future__ import division, print_function
import numpy as np
-
import bilby
-# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
-
+# Set the duration and sampling frequency of the data segment that we're
+# going to inject the signal into
duration = 4.
sampling_frequency = 2048.
@@ -24,22 +25,24 @@ bilby.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(88170235)
-# We are going to inject a binary black hole waveform. We first establish a dictionary of parameters that
-# includes all of the different waveform parameters, including masses of the two black holes (mass_1, mass_2),
+# We are going to inject a binary black hole waveform. We first establish a
+# dictionary of parameters that includes all of the different waveform
+# parameters, including masses of the two black holes (mass_1, mass_2),
# spins of both black holes (a, tilt, phi), etc.
-injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
- luminosity_distance=2000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
- ra=1.375, dec=-1.2108)
+injection_parameters = dict(
+ mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0,
+ phi_12=1.7, phi_jl=0.3, luminosity_distance=2000., iota=0.4, psi=2.659,
+ phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108)
# Fixed arguments passed into the source model
waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
- reference_frequency=50.)
+ reference_frequency=50., minimum_frequency=20.)
# Create the waveform_generator using a LAL BinaryBlackHole source function
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
- parameters=injection_parameters, waveform_arguments=waveform_arguments)
+ waveform_arguments=waveform_arguments)
# Set up interferometers. In this case we'll use two interferometers
# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design
@@ -51,30 +54,35 @@ ifos.set_strain_data_from_power_spectral_densities(
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
-# Set up prior, which is a dictionary
-# By default we will sample all terms in the signal models. However, this will take a long time for the calculation,
-# so for this example we will set almost all of the priors to be equall to their injected values. This implies the
-# prior is a delta function at the true, injected value. In reality, the sampler implementation is smart enough to
-# not sample any parameter that has a delta-function prior.
-# The above list does *not* include mass_1, mass_2, iota and luminosity_distance, which means those are the parameters
-# that will be included in the sampler. If we do nothing, then the default priors get used.
+# Set up a PriorSet, which inherits from dict.
+# By default we will sample all terms in the signal models. However, this will
+# take a long time for the calculation, so for this example we will set almost
+# all of the priors to be equall to their injected values. This implies the
+# prior is a delta function at the true, injected value. In reality, the
+# sampler implementation is smart enough to not sample any parameter that has
+# a delta-function prior.
+# The above list does *not* include mass_1, mass_2, iota and luminosity
+# distance, which means those are the parameters that will be included in the
+# sampler. If we do nothing, then the default priors get used.
priors = bilby.gw.prior.BBHPriorSet()
priors['geocent_time'] = bilby.core.prior.Uniform(
minimum=injection_parameters['geocent_time'] - 1,
maximum=injection_parameters['geocent_time'] + 1,
name='geocent_time', latex_label='$t_c$', unit='$s$')
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra', 'dec', 'geocent_time', 'phase']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra',
+ 'dec', 'geocent_time', 'phase']:
priors[key] = injection_parameters[key]
-# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = bilby.gw.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator,
- time_marginalization=False, phase_marginalization=False,
- distance_marginalization=False, prior=priors)
+# Initialise the likelihood by passing in the interferometer data (ifos) and
+# the waveoform generator
+likelihood = bilby.gw.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler. In this case we're going to use the `dynesty` sampler
-result = bilby.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
- injection_parameters=injection_parameters, outdir=outdir, label=label)
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
-# make some plots of the outputs
+# Make a corner plot.
result.plot_corner()
diff --git a/examples/injection_examples/change_sampled_parameters.py b/examples/injection_examples/change_sampled_parameters.py
index f629b2c91..aabcbfebc 100644
--- a/examples/injection_examples/change_sampled_parameters.py
+++ b/examples/injection_examples/change_sampled_parameters.py
@@ -29,6 +29,8 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
reference_frequency=50.)
# Create the waveform_generator using a LAL BinaryBlackHole source function
+# We specify a function which transforms a dictionary of parameters into the
+# appropriate parameters for the source model.
waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
sampling_frequency=sampling_frequency, duration=duration,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
@@ -45,6 +47,8 @@ ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
# Set up prior
+# Note it is possible to sample in different parameters to those that were
+# injected.
priors = bilby.gw.prior.BBHPriorSet()
priors.pop('mass_1')
priors.pop('mass_2')
@@ -69,6 +73,8 @@ likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler
+# Note we've added a post-processing conversion function, this will generate
+# many useful additional parameters, e.g., source-frame masses.
result = bilby.core.sampler.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty', outdir=outdir,
injection_parameters=injection_parameters, label='DifferentParameters',
diff --git a/examples/injection_examples/create_your_own_source_model.py b/examples/injection_examples/create_your_own_source_model.py
index 254fdfc52..4e0ab71da 100644
--- a/examples/injection_examples/create_your_own_source_model.py
+++ b/examples/injection_examples/create_your_own_source_model.py
@@ -22,13 +22,14 @@ def sine_gaussian(f, A, f0, tau, phi0, geocent_time, ra, dec, psi):
return {'plus': plus, 'cross': cross}
-# We now define some parameters that we will inject and then a waveform generator
+# We now define some parameters that we will inject
injection_parameters = dict(A=1e-23, f0=100, tau=1, phi0=0, geocent_time=0,
ra=0, dec=0, psi=0)
-waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(duration=duration,
- sampling_frequency=sampling_frequency,
- frequency_domain_source_model=sine_gaussian,
- parameters=injection_parameters)
+
+# Now we pass our source function to the WaveformGenerator
+waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=sine_gaussian)
# Set up interferometers.
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
@@ -41,10 +42,11 @@ ifos.inject_signal(waveform_generator=waveform_generator,
# Here we define the priors for the search. We use the injection parameters
# except for the amplitude, f0, and geocent_time
prior = injection_parameters.copy()
-prior['A'] = bilby.core.prior.PowerLaw(alpha=-1, minimum=1e-25, maximum=1e-21, name='A')
+prior['A'] = bilby.core.prior.LogUniform(minimum=1e-25, maximum=1e-21, name='A')
prior['f0'] = bilby.core.prior.Uniform(90, 110, 'f')
-likelihood = bilby.gw.likelihood.GravitationalWaveTransient(ifos, waveform_generator)
+likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
result = bilby.core.sampler.run_sampler(
likelihood, prior, sampler='dynesty', outdir=outdir, label=label,
diff --git a/examples/injection_examples/create_your_own_time_domain_source_model.py b/examples/injection_examples/create_your_own_time_domain_source_model.py
index 5ea57fb58..28bae6952 100644
--- a/examples/injection_examples/create_your_own_time_domain_source_model.py
+++ b/examples/injection_examples/create_your_own_time_domain_source_model.py
@@ -1,41 +1,41 @@
#!/usr/bin/env python
"""
A script to show how to create your own time domain source model.
-A simple damped Gaussian signal is defined in the time domain, injected into noise in
-two interferometers (LIGO Livingston and Hanford at design sensitivity),
-and then recovered.
+A simple damped Gaussian signal is defined in the time domain, injected into
+noise in two interferometers (LIGO Livingston and Hanford at design
+sensitivity), and then recovered.
"""
-import bilby
import numpy as np
-
+import bilby
# define the time-domain model
-def time_domain_damped_sinusoid(time, amplitude, damping_time, frequency, phase, ra, dec, psi, geocent_time):
+def time_domain_damped_sinusoid(
+ time, amplitude, damping_time, frequency, phase):
"""
- This example only creates a linearly polarised signal with only plus polarisation.
+ This example only creates a linearly polarised signal with only plus
+ polarisation.
"""
-
- plus = amplitude * np.exp(-time / damping_time) * np.sin(2.*np.pi*frequency*time + phase)
+ plus = amplitude * np.exp(-time / damping_time) *\
+ np.sin(2 * np.pi * frequency * time + phase)
cross = np.zeros(len(time))
-
return {'plus': plus, 'cross': cross}
+
# define parameters to inject.
injection_parameters = dict(amplitude=5e-22, damping_time=0.1, frequency=50,
- phase=0,
- ra=0, dec=0, psi=0, geocent_time=0.)
+ phase=0, ra=0, dec=0, psi=0, geocent_time=0.)
duration = 0.5
sampling_frequency = 2048
-outdir='outdir'
-label='time_domain_source_model'
+outdir = 'outdir'
+label = 'time_domain_source_model'
# call the waveform_generator to create our waveform model.
-waveform = bilby.gw.waveform_generator.WaveformGenerator(duration=duration, sampling_frequency=sampling_frequency,
- time_domain_source_model=time_domain_damped_sinusoid,
- parameters=injection_parameters)
+waveform = bilby.gw.waveform_generator.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ time_domain_source_model=time_domain_damped_sinusoid)
# inject the signal into three interferometers
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
@@ -47,7 +47,7 @@ ifos.inject_signal(waveform_generator=waveform,
# create the priors
prior = injection_parameters.copy()
-prior['amplitude'] = bilby.core.prior.Uniform(1e-23, 1e-21, r'$h_0$')
+prior['amplitude'] = bilby.core.prior.LogUniform(1e-23, 1e-21, r'$h_0$')
prior['damping_time'] = bilby.core.prior.Uniform(
0, 1, r'damping time', unit='$s$')
prior['frequency'] = bilby.core.prior.Uniform(0, 200, r'frequency', unit='Hz')
@@ -57,9 +57,9 @@ prior['phase'] = bilby.core.prior.Uniform(-np.pi / 2, np.pi / 2, r'$\phi$')
likelihood = bilby.gw.likelihood.GravitationalWaveTransient(ifos, waveform)
# launch sampler
-result = bilby.core.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=1000,
- injection_parameters=injection_parameters,
- outdir=outdir, label=label)
+result = bilby.core.sampler.run_sampler(
+ likelihood, prior, sampler='dynesty', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
diff --git a/examples/injection_examples/eccentric_inspiral.py b/examples/injection_examples/eccentric_inspiral.py
index 75ff93b57..2034df2b5 100644
--- a/examples/injection_examples/eccentric_inspiral.py
+++ b/examples/injection_examples/eccentric_inspiral.py
@@ -1,8 +1,8 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate running parameter estimation on a reduced parameter space
-for an injected eccentric binary black hole signal with masses & distnace similar
-to GW150914.
+Tutorial to demonstrate running parameter estimation on a reduced parameter
+space for an injected eccentric binary black hole signal with masses & distnace
+similar to GW150914.
This uses the same binary parameters that were used to make Figures 1, 2 & 5 in
Lower et al. (2018) -> arXiv:1806.05350.
@@ -10,14 +10,11 @@ Lower et al. (2018) -> arXiv:1806.05350.
For a more comprehensive look at what goes on in each step, refer to the
"basic_tutorial.py" example.
"""
-from __future__ import division, print_function
+from __future__ import division
import numpy as np
-
import bilby
-import matplotlib.pyplot as plt
-
duration = 64.
sampling_frequency = 256.
@@ -28,13 +25,15 @@ bilby.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility.
np.random.seed(150914)
-injection_parameters = dict(mass_1=35., mass_2=30., eccentricity=0.1,
- luminosity_distance=440., iota=0.4, psi=0.1, phase=1.2,
- geocent_time=1180002601.0, ra=45, dec=5.73)
+injection_parameters = dict(
+ mass_1=35., mass_2=30., eccentricity=0.1, luminosity_distance=440.,
+ iota=0.4, psi=0.1, phase=1.2, geocent_time=1180002601.0, ra=45, dec=5.73)
-waveform_arguments = dict(waveform_approximant='EccentricFD', reference_frequency=10., minimum_frequency=10.)
+waveform_arguments = dict(waveform_approximant='EccentricFD',
+ reference_frequency=10., minimum_frequency=10.)
-# Create the waveform_generator using the LAL eccentric black hole no spins source function
+# Create the waveform_generator using the LAL eccentric black hole no spins
+# source function
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=bilby.gw.source.lal_eccentric_binary_black_hole_no_spins,
@@ -43,8 +42,8 @@ waveform_generator = bilby.gw.WaveformGenerator(
# Setting up three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1), and
# Virgo (V1)) at their design sensitivities. The maximum frequency is set just
-# prior to the point at which the waveform model terminates. This is to avoid any
-# biases introduced from using a sharply terminating waveform model.
+# prior to the point at which the waveform model terminates. This is to avoid
+# any biases introduced from using a sharply terminating waveform model.
minimum_frequency = 10.
maximum_frequency = 128.
@@ -59,13 +58,13 @@ ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
# Now we set up the priors on each of the binary parameters.
-priors = dict()
+priors = bilby.core.prior.PriorSet()
priors["mass_1"] = bilby.core.prior.Uniform(
name='mass_1', minimum=5, maximum=60, unit='$M_{\\odot}$')
priors["mass_2"] = bilby.core.prior.Uniform(
name='mass_2', minimum=5, maximum=60, unit='$M_{\\odot}$')
-priors["eccentricity"] = bilby.core.prior.PowerLaw(
- name='eccentricity', latex_label='$e$', alpha=-1, minimum=1e-4, maximum=0.4)
+priors["eccentricity"] = bilby.core.prior.LogUniform(
+ name='eccentricity', latex_label='$e$', minimum=1e-4, maximum=0.4)
priors["luminosity_distance"] = bilby.gw.prior.UniformComovingVolume(
name='luminosity_distance', minimum=1e2, maximum=2e3)
priors["dec"] = bilby.core.prior.Cosine(name='dec')
@@ -79,15 +78,13 @@ priors["geocent_time"] = bilby.core.prior.Uniform(
1180002600.9, 1180002601.1, name='geocent_time', unit='s')
# Initialising the likelihood function.
-likelihood = bilby.gw.likelihood.GravitationalWaveTransient(interferometers=ifos,
- waveform_generator=waveform_generator, time_marginalization=False,
- phase_marginalization=False, distance_marginalization=False,
- prior=priors)
+likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
# Now we run sampler (PyMultiNest in our case).
-result = bilby.run_sampler(likelihood=likelihood, priors=priors, sampler='pymultinest',
- npoints=1000, injection_parameters=injection_parameters,
- outdir=outdir, label=label)
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='pymultinest', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
# And finally we make some plots of the output posteriors.
result.plot_corner()
diff --git a/examples/injection_examples/how_to_specify_the_prior.py b/examples/injection_examples/how_to_specify_the_prior.py
index b92288d90..4c9b80635 100644
--- a/examples/injection_examples/how_to_specify_the_prior.py
+++ b/examples/injection_examples/how_to_specify_the_prior.py
@@ -1,12 +1,12 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate how to specify the prior distributions used for parameter estimation.
+Tutorial to demonstrate how to specify the prior distributions used for
+parameter estimation.
"""
from __future__ import division, print_function
-import bilby
-import numpy as np
-import bilby.gw.prior
+import numpy as np
+import bilby
duration = 4.
@@ -15,18 +15,19 @@ outdir = 'outdir'
np.random.seed(151012)
-injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
- luminosity_distance=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
- ra=1.375, dec=-1.2108)
+injection_parameters = dict(
+ mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0,
+ phi_12=1.7, phi_jl=0.3, luminosity_distance=4000., iota=0.4, psi=2.659,
+ phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108)
waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
- reference_frequency=50.)
+ reference_frequency=50., minimum_frequency=20.)
# Create the waveform_generator using a LAL BinaryBlackHole source function
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
- parameters=injection_parameters, waveform_arguments=waveform_arguments)
+ waveform_arguments=waveform_arguments)
# Set up interferometers.
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
@@ -58,14 +59,17 @@ priors['a_2'] = bilby.core.prior.Interped(
name='a_2', xx=a_2, yy=p_a_2, minimum=0, maximum=0.5)
# Additionally, we have Gaussian, TruncatedGaussian, Sine and Cosine.
# It's also possible to load an interpolate a prior from a file.
-# Finally, if you don't specify any necessary parameters it will be filled in from the default when the sampler starts.
+# Finally, if you don't specify any necessary parameters it will be filled in
+# from the default when the sampler starts.
# Enjoy.
# Initialise GravitationalWaveTransient
-likelihood = bilby.gw.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator)
+likelihood = bilby.gw.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler
-result = bilby.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, outdir=outdir, label='specify_prior')
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty', outdir=outdir,
+ injection_parameters=injection_parameters, label='specify_prior')
result.plot_corner()
diff --git a/examples/injection_examples/marginalized_likelihood.py b/examples/injection_examples/marginalized_likelihood.py
index 83259d627..29eae13c6 100644
--- a/examples/injection_examples/marginalized_likelihood.py
+++ b/examples/injection_examples/marginalized_likelihood.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate how to improve the speed and efficiency of parameter estimation on an injected signal using
-phase and distance marginalisation.
+Tutorial to demonstrate how to improve the speed and efficiency of parameter
+estimation on an injected signal using time, phase and distance marginalisation.
"""
from __future__ import division, print_function
import bilby
@@ -11,12 +11,14 @@ import numpy as np
duration = 4.
sampling_frequency = 2048.
outdir = 'outdir'
+label = 'marginalized_likelihood'
np.random.seed(170608)
-injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
- luminosity_distance=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
- ra=1.375, dec=-1.2108)
+injection_parameters = dict(
+ mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0,
+ phi_12=1.7, phi_jl=0.3, luminosity_distance=4000., iota=0.4, psi=2.659,
+ phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108)
waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
reference_frequency=50.)
@@ -24,7 +26,7 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
# Create the waveform_generator using a LAL BinaryBlackHole source function
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
- frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, parameters=injection_parameters,
+ frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
waveform_arguments=waveform_arguments)
# Set up interferometers.
@@ -38,19 +40,22 @@ ifos.inject_signal(waveform_generator=waveform_generator,
# Set up prior
priors = bilby.gw.prior.BBHPriorSet()
# These parameters will not be sampled
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra', 'dec', 'geocent_time']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra',
+ 'dec']:
priors[key] = injection_parameters[key]
# Initialise GravitationalWaveTransient
-# Note that we now need to pass the: priors and flags for each thing that's being marginalised.
-# This is still under development so care should be taken with the marginalised likelihood.
+# Note that we now need to pass the: priors and flags for each thing that's
+# being marginalised. A lookup table is used fro distance marginalisation which
+# takes a few minutes to build.
likelihood = bilby.gw.GravitationalWaveTransient(
interferometers=ifos, waveform_generator=waveform_generator, prior=priors,
- distance_marginalization=False, phase_marginalization=True,
- time_marginalization=False)
+ distance_marginalization=True, phase_marginalization=True,
+ time_marginalization=True)
# Run sampler
-result = bilby.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, outdir=outdir, label='MarginalisedLikelihood')
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty',
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
diff --git a/examples/injection_examples/sine_gaussian_example.py b/examples/injection_examples/sine_gaussian_example.py
index 4bbf7c6b0..2f0d1a127 100644
--- a/examples/injection_examples/sine_gaussian_example.py
+++ b/examples/injection_examples/sine_gaussian_example.py
@@ -1,13 +1,14 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate running parameter estimation on a sine gaussian injected signal.
-
+Tutorial to demonstrate running parameter estimation on a sine gaussian
+injected signal.
"""
from __future__ import division, print_function
import bilby
import numpy as np
-# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+# Set the duration and sampling frequency of the data segment that we're going
+# to inject the signal into
duration = 4.
sampling_frequency = 2048.
@@ -19,19 +20,21 @@ bilby.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(170801)
-# We are going to inject a sine gaussian waveform. We first establish a dictionary of parameters that
-# includes all of the different waveform parameters
-injection_parameters = dict(hrss = 1e-22, Q = 5.0, frequency = 200.0, ra = 1.375, dec = -1.2108,
- geocent_time = 1126259642.413, psi= 2.659)
+# We are going to inject a sine gaussian waveform. We first establish a
+# dictionary of parameters that includes all of the different waveform
+# parameters
+injection_parameters = dict(
+ hrss=1e-22, Q=5.0, frequency=200.0, ra=1.375, dec=-1.2108,
+ geocent_time=1126259642.413, psi=2.659)
# Create the waveform_generator using a sine Gaussian source function
-waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(duration=duration,
- sampling_frequency=sampling_frequency,
- frequency_domain_source_model=bilby.gw.source.sinegaussian,
- parameters=injection_parameters)
+waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=bilby.gw.source.sinegaussian)
-# Set up interferometers. In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
-# and Virgo (V1)). These default to their design sensitivity
+# Set up interferometers. In this case we'll use three interferometers
+# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)). These default to
+# their design sensitivity
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
@@ -41,27 +44,22 @@ ifos.inject_signal(waveform_generator=waveform_generator,
# Set up prior, which is a dictionary
priors = dict()
-# By default we will sample all terms in the signal models. However, this will take a long time for the calculation,
-# so for this example we will set almost all of the priors to be equall to their injected values. This implies the
-# prior is a delta function at the true, injected value. In reality, the sampler implementation is smart enough to
-# not sample any parameter that has a delta-function prior.
for key in ['psi', 'ra', 'dec', 'geocent_time']:
priors[key] = injection_parameters[key]
-# The above list does *not* include frequency and Q, which means those are the parameters
-# that will be included in the sampler. If we do nothing, then the default priors get used.
-#priors['Q'] = bilby.prior.create_default_prior(name='Q')
-#priors['frequency'] = bilby.prior.create_default_prior(name='frequency')
priors['Q'] = bilby.core.prior.Uniform(2, 50, 'Q')
priors['frequency'] = bilby.core.prior.Uniform(30, 1000, 'frequency', unit='Hz')
priors['hrss'] = bilby.core.prior.Uniform(1e-23, 1e-21, 'hrss')
-# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = bilby.gw.likelihood.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator)
+# Initialise the likelihood by passing in the interferometer data (IFOs) and
+# the waveoform generator
+likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler. In this case we're going to use the `dynesty` sampler
-result = bilby.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
- injection_parameters=injection_parameters, outdir=outdir, label=label)
+result = bilby.core.sampler.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
# make some plots of the outputs
result.plot_corner()
diff --git a/examples/injection_examples/using_gwin.py b/examples/injection_examples/using_gwin.py
index a213c490c..dc312ddd8 100644
--- a/examples/injection_examples/using_gwin.py
+++ b/examples/injection_examples/using_gwin.py
@@ -48,7 +48,8 @@ fixed_parameters = injection_parameters.copy()
for key in priors:
fixed_parameters.pop(key)
-# These lines generate the `model` object - see https://gwin.readthedocs.io/en/latest/api/gwin.models.gaussian_noise.html
+# These lines generate the `model` object - see
+# https://gwin.readthedocs.io/en/latest/api/gwin.models.gaussian_noise.html
generator = FDomainDetFrameGenerator(
FDomainCBCGenerator, 0.,
variable_args=variable_parameters, detectors=['H1', 'L1'],
@@ -64,6 +65,7 @@ model.update(**injection_parameters)
# This create a dummy class to convert the model into a bilby.likelihood object
class GWINLikelihood(bilby.core.likelihood.Likelihood):
+
def __init__(self, model):
""" A likelihood to wrap around GWIN model objects
diff --git a/examples/other_examples/linear_regression_pymc3.py b/examples/other_examples/linear_regression_pymc3.py
index c10d5d3c9..df98e5a5b 100644
--- a/examples/other_examples/linear_regression_pymc3.py
+++ b/examples/other_examples/linear_regression_pymc3.py
@@ -18,10 +18,12 @@ label = 'linear_regression_pymc3'
outdir = 'outdir'
bilby.utils.check_directory_exists_and_if_not_mkdir(outdir)
+
# First, we define our "signal model", in this case a simple linear function
def model(time, m, c):
return time * m + c
+
# Now we define the injection parameters which we make simulated data with
injection_parameters = dict(m=0.5, c=0.2)
@@ -51,7 +53,7 @@ likelihood = GaussianLikelihood(time, data, model, sigma=sigma)
# From hereon, the syntax is exactly equivalent to other bilby examples
# We make a prior
-priors = {}
+priors = dict()
priors['m'] = bilby.core.prior.Uniform(0, 5, 'm')
priors['c'] = bilby.core.prior.Uniform(-2, 2, 'c')
diff --git a/examples/other_examples/linear_regression_pymc3_custom_likelihood.py b/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
index 9321ea4c8..f62b187ac 100644
--- a/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
+++ b/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
@@ -20,10 +20,12 @@ label = 'linear_regression_pymc3_custom_likelihood'
outdir = 'outdir'
bilby.utils.check_directory_exists_and_if_not_mkdir(outdir)
+
# First, we define our "signal model", in this case a simple linear function
def model(time, m, c):
return time * m + c
+
# Now we define the injection parameters which we make simulated data with
injection_parameters = dict(m=0.5, c=0.2)
@@ -51,6 +53,7 @@ fig.savefig('{}/{}_data.png'.format(outdir, label))
# Parameter estimation: we now define a Gaussian Likelihood class relevant for
# our model.
class GaussianLikelihoodPyMC3(bilby.Likelihood):
+
def __init__(self, x, y, sigma, function):
"""
A general Gaussian likelihood - the parameters are inferred from the
@@ -105,6 +108,7 @@ class GaussianLikelihoodPyMC3(bilby.Likelihood):
# set the likelihood distribution
pm.Normal('likelihood', mu=mu, sd=self.sigma, observed=self.y)
+
# Now lets instantiate a version of our GaussianLikelihood, giving it
# the time, data and signal model
likelihood = GaussianLikelihoodPyMC3(time, data, sigma, model)
@@ -144,6 +148,6 @@ priors['c'] = PriorPyMC3(-2, 2, 'c')
# And run sampler
result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='pymc3', draws=1000,
- tune=1000, discard_tuned_samples=True, injection_parameters=injection_parameters,
- outdir=outdir, label=label)
+ tune=1000, discard_tuned_samples=True,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
diff --git a/examples/supernova_example/supernova_example.py b/examples/supernova_example/supernova_example.py
index b6eeb99a1..05b2eebcd 100644
--- a/examples/supernova_example/supernova_example.py
+++ b/examples/supernova_example/supernova_example.py
@@ -1,4 +1,4 @@
-#!/bin/python
+#!/usr/bin/env python
"""
Tutorial to demonstrate running parameter estimation/model selection on an NR
@@ -9,10 +9,10 @@ factor. (See https://arxiv.org/pdf/1202.3256.pdf)
"""
from __future__ import division, print_function
import numpy as np
+import bilby
-# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
-import bilby.gw.likelihood
-
+# Set the duration and sampling frequency of the data segment that we're going
+# to inject the signal into
duration = 3.
sampling_frequency = 4096.
--
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